Data-driven decision making is widely employed nowadays by businesses, governments and other organizations in order to optimize efficiency and effectiveness of their operations. Decisions once undertaken by humans are increasingly conducted by algorithms, derived through Machine Learning (ML) and Artificial Intelligence (AI) powered by big data. The technology has already penetrated into almost all spheres of human life, from content recommendation and healthcare to predictive policing and autonomous driving, deeply affecting everyone, anywhere, anytime. While data-driven decision making allows previously unthinkable optimizations in the automation of expensive human decision making, the risks that the technology can pose are also high, leading to an ever increasing public concern about the impact of the technology in our lives. In this talk I will argue that many of these risks are the result of misconceptions and violated assumptions and therefore, an accurate understanding of the technology is essential for benefiting from its huge potential while ensuring human values and social good.

In the context of this talk, I will focus on two particular issues:

The assumption that data come from a stationary distribution. In reality, data are not born in batch, rather they are created as a stream and therefore adaptive machine learning solutions are required to ensure that what is learned is up-to-date and valid.

The common misconception that "humans are subjective, but data and algorithms not and therefore they cannot discriminate". In reality, the fact that data-driven decision making systems learn from data does not guarantee that their outputs will be free of human biases or discrimination.

For each aspect, I will discuss the challenges for data-driven learning and I will present novel solutions to overcome their limitations.